@Marvin I have a parent flow and inside that I’m calling a child flow multiple times with different parameters. Each call to this child flow is independent and they can be run in parallel. I’m using Kubernetes job pool so I want these child flow calls to be executed in their separate Kubernetes job pods in parallel. How to do this?

It seems that I encountered some hiccups while trying to fetch specific information and GitHub issues for you. However, let's break down your query and see how to set up your flows for parallel execution in Kubernetes. If you're using Prefect 2.x or 3.x, you generally want to make sure you configure your child flows to be run asynchronously. This can be managed using

prefect

configurations for Kubernetes jobs. Here’s a general outline: 1. Define Your Flows: - Create your parent flow that calls your child flows. - Ensure each child flow call is wrapped in an async function to allow parallel execution. 2. Kubernetes Configuration: - Use a Kubernetes job to execute each child flow. This typically involves setting the Kubernetes run configuration in Prefect to specify the job template that Prefect will use. - Configure your flow to run with the Kubernetes orchestrator. This can be done by setting the flow.run_config to use

KubernetesRun

. Here is a conceptual code example to direct you:

python
from prefect import task, flow

@task
def some_task(params):
    print(f"Running task with {params}")

@flow
def child_flow(params):
    some_task(params)

@flow
def parent_flow():
    params_list = [{"param1": "value1"}, {"param1": "value2"}, {"param1": "value3"}]  # Example parameters
    for params in params_list:
        child_flow(params)

# Ensure this is configured to use Kubernetes with the relevant job definitions.

Ensure you consult the Prefect documentation to get the precise setup for your Kubernetes environment. You may still need to adapt configurations like

KubernetesRun

to match your exact job specifications, including resource requests, limits, and other Kubernetes-specific settings. For detailed setups or troubleshooting, I recommend checking the Prefect docs linked below: - Prefect 2.x Documentation - Prefect 3.x Documentation If you encounter any further issues or need more detailed instructions, feel free to reach out to the Prefect community or check with their support. And remember, even robots can hit a few bumps on the way to perfect parallel computing—but unlike them, we have caffeine to fix our issues!

@Marvin i don't want each child flow run to wait each other

I found some useful insights to help you run child flows in parallel in a Kubernetes job pool using Prefect. The key is to leverage asynchronous execution and the appropriate configuration for Kubernetes. 1. Asynchronous Execution: In Prefect 2.x and 3.x, nested or child flows run synchronously by default, meaning they will block the execution of the parent flow until completion. However, you can use asynchronous execution to allow child flows to run concurrently. This can be achieved using Python’s async features like

asyncio.gather

. For more details, see the documentation: Prefect 3.x - Writing Flows. 2. Kubernetes Job Pool: Prefect allows you to configure flows to run as Kubernetes jobs. Ensure that each child flow run is assigned to a separate Kubernetes pod by configuring the Kubernetes Run infrastructure. You can customize the job templates in the Prefect configuration to ensure proper resource allocation. For detailed setup, check: - Prefect 2.x - Kubernetes Deployment Guide - Prefect 3.x - Kubernetes Infrastructure Example 3. Example Configuration: Here’s a small example conceptual outline (not direct code) to guide you: - Use the

@flow

decorator to define both parent and child flows. - Utilize Python’s async functionality to ensure child flows run asynchronously:

python
     from prefect import flow, task
     import asyncio

     @task
     def do_something(param):
         # Your task logic here
         pass

     @flow
     async def child_flow(param):
         do_something(param)

     @flow
     async def parent_flow():
         params_list = [{"param1": "value1"}, {"param1": "value2"}]
         await asyncio.gather(*(child_flow(param) for param in params_list))

By following these practices, you'll be able to run child flow calls in parallel on separate Kubernetes job pods. If you need further guidance, the Prefect community is an excellent resource. Remember, even the best-laid plans of flows and pods can hit a glitch... but that's why we have asynchronous retries!